Performance tuning parameters
- ESTIMATED_CHUNKS: Set according to the number of pages of the document (e.g., 50 for a 100-page document).
- RECURSION_LIMIT: Controls the recursion depth of entity disambiguation (default 10).
- BATCH_SIZE: Adjust the number of text blocks processed by LLM (affects memory footprint)
Hardware Configuration Recommendations
For documents over 200 pages: 1) Allocate at least 16GB of memory; 2) Use SSD storage to accelerate chunking; 3) Consider multi-GPU parallelism (requires modification)docker-compose.yml). Tested 70% speed increase after optimization when processing legal contracts.
error handling scheme
In case of process interruption: 1) check Fuseki logs to confirm storage space; 2) verify PDF parsing integrity (available)pdfinfotool); 3) manually merging ternary groups after processing documents in segments.
This answer comes from the articleOntoCast: an intelligent framework for extracting semantic triples from documentsThe
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